Digital cameras have become widely accepted in the marketplace. Most users have a large number of digital images in their collections, often residing in unorganized folders on their home computer. Typically, the images are stored with meaningless names representing the frame number for the digital camera on which they were captured. A large frustration for many users is being able to find an image that they are looking for in their image collection, which may contain thousands of images. As a result, many images sit unused.
In order to enable easier retrieval of digital images stored in digital image collections, it is desirable to be able to classify pictorial images according to attributes such as event type, subject and the like. This is an important step to enable a more satisfying user experience for the viewing and use of digital images.
There is an extensive body of prior art addressing image classification methods. For example, L. J. Li and L. Fei-Fei have proposed a method to classify events in images by integrating scene and object categorizations in their published article, “What, Where and Who? Classifying Events by Scene and Object Recognition” (Proceedings of Eleventh IEEE International Conference on Computer Vision, pp. 1-8, 2007).
Another example of an image classification method would include U.S. Pat. No. 6,915,011 by A. Loui, et al. which describes an event clustering method using foreground and background segmentation.
One problem with the prior art methods is that it is often difficult to distinguish between objects which may have similar attributes. For example, a large red area in an image may correspond to a red shirt, a red barn or a sunset sky. One scene attribute that would make it easier to distinguish between different types of image content would be range information corresponding to the distance of objects in the scene from the viewpoint. Most digital images do not have range information available, although developing cameras that capture range information is an area of active research. But even when range information is available, it has not been used in any image classification methods. Consequently, a need exists in the art for an image classification using range information.